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Research on Activity-based Device-Free Human Identification Approach
YANG Yu 1,JIANG Ting 2 *,DING Xue 2,ZHONG Yi 2
1.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876;School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876;School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876;School of Information and Electronics, Beijing Institute of Technology, Beijing 100081
2.
*Correspondence author
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Funding: National Natural Science Foundation of China （No.No. 62071061, 61671075, 61631003) and Beijing Institute of Technology Research Fund Program for Young Scholar）
Opened online: 8 April 2021
Accepted by: none
Citation: YANG Yu,JIANG Ting,DING Xue.Research on Activity-based Device-Free Human Identification Approach[OL]. [ 8 April 2021] http://en.paper.edu.cn/en_releasepaper/content/4754417
 
 
In recent years, with the development of device-free sensing (DFS), researchers have applied it to the field of identity recognition. By taking advantage of the unique influence that different people have on surrounding wireless signals, the technology is able to identify people in a contactless way. Some progress has been made in identification by analyzing the gait information in the received wireless signals, but it needs to provide enough space for walking, which limits its application scenarios to some extent. In order to solve this problem, this paper proposes WAID, a recognition system based on human activities. Through analyzing the unique influence of different people doing the same activity on the channel state information (CSI) of WiFi signal, the identity information of people can be extracted from it. Thus, identification can be achieved by performing activities in fixed locations where there are no area requirements. The experimental results show that in the case of 2 to 6 people, the average recognition accuracy of WAID is 94.3% to 88%, and the average accuracy under six positions is 84.8%.
Keywords:DFS; Human Identification; WiFi signals; CSI
 
 
 

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